| CISIS {MFSIS} | R Documentation |
Model-Free Feature screening Based on Concordance Index for Ultra-High Dimensional Categorical Data
Description
The proposed method is based on the concordance index which measures concordance between random vectors. A model-free and robust feature screening method for ultrahigh-dimensional categorical data. The performance is quite robust in the presence of heavy-tailed distributions, extremely unbalance responses, and category-adaptive data.
Usage
CISIS(X, Y, nsis)
Arguments
X |
The design matrix of dimensions n * p. Each row is an observation vector. |
Y |
The response vector of dimension n * 1. |
nsis |
Number of predictors recruited by CISIS. The default is n/log(n). |
Value
the labels of first nsis largest active set of all predictors
Author(s)
Xuewei Cheng xwcheng@hunnu.edu.cn
References
Cheng X, Wang H. A generic model-free feature screening procedure for ultra-high dimensional data with categorical response[J]. Computer Methods and Programs in Biomedicine, 2023, 229: 107269.
Examples
n <- 100
p <- 200
rho <- 0.5
data <- GendataLGM(n, p, rho)
data <- cbind(data[[1]], data[[2]])
colnames(data)[1:ncol(data)] <- c(paste0("X", 1:(ncol(data) - 1)), "Y")
data <- as.matrix(data)
X <- data[, 1:(ncol(data) - 1)]
Y <- data[, ncol(data)]
A <- CISIS(X, Y, n / log(n))
A